Sunday, 18 October 2015

With
the increasing volume of images users share through social sites, maintaining
privacy has become a major problem, as demonstrated by a recent wave of
publicized incidents where users inadvertently shared personal information. In
light of these incidents, the need of tools to help users control access to
their shared content is apparent. Toward addressing this need, we propose an
Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy
settings for their images. We examine the role of social context, image
content, and metadata as possible indicators of users’ privacy preferences. We
propose a two-level framework which according to the user’s available history
on the site, determines the best available privacy policy for the user’s images
being uploaded. Our solution relies on an image classification framework for
image categories which may be associated with similar policies, and on a policy
prediction algorithm to automatically generate a policy for each newly uploaded
image, also according to users’ social features. Over time, the generated
policies will follow the evolution of users’ privacy attitude. We provide the
results of our extensive evaluation over 5,000 policies, which demonstrate the
effectiveness of our system, with prediction accuracies over 90 percent.

Aim

The
main aim is to propose an Adaptive Privacy Policy Prediction (A3P) system to
help users compose privacy settings for the images shared through social sites.

Scope

The
scope of the project is on an image classification framework for image
categories which may be associated with similar policies, and on a policy
prediction algorithm to automatically generate a policy for each newly uploaded
image, also according to users’ social features.

Existing System

There
is a large body of work on image content analysis, for classification and
interpretation retrieval and photo ranking also in the context of online photo
sharing sites. Of these works, existing work is probably the closest to ours.
The existing system explores privacy-aware image classification using a mixed
set of features, both content and meta-data.

Disadvantages

·This
is however a binary classification (private versus public), so the
classification task is very different than ours.

·Existing
proposals for automating privacy settings appear to be inadequate to address
the unique privacy needs of images due to the amount of information implicitly
carried within images, and their relationship with the online environment
wherein they are exposed.

·Users
struggle to set up and maintain privacy settings in the most content sharing
websites.

Proposed System

Proposed
system is, an Adaptive Privacy Policy Prediction (A3P) system which aims to
provide users a hassle free privacy settings experience by automatically
generating personalized policies. The A3P system handles user uploaded images,
and factors in the following criteria that influence one’s privacy settings of
images.

·The
impact of social environment and personal characteristics.

·The
role of image’s content and metadata.

Advantages

The
A3P system provides a comprehensive framework to infer privacy preferences
based on the information available for a given user. A3P also effectively
tackled the issue of cold-start, leveraging social context information. This
project proves that our A3P is a practical tool that offers significant
improvements over current approaches to privacy.